North Region · Applied AI
Semantic search (RAG) in Woodlands
AI search over your data, built for businesses operating in Woodlands.
What semantic search (RAG) actually does
Search that understands intent, not just keywords. Your team types what they mean – and gets the right document, ticket, or product from across every system, with citations.
The version of semantic search (RAG) that works for a Woodlands business is rarely the version a national vendor would sell you. We build the one that fits how your team actually operates – usually with fewer parts than the off-the-shelf pitch.
- 01 Indexes Drive, SharePoint, Notion, Slack, your CRM
- 02 Returns answers with source links – no hallucinations
- 03 Permissioned so staff only see what they should
- 04 Re-indexes nightly so results stay fresh
Built on: Pinecone Claude Postgres pgvector Vercel AI SDK
Why Woodlands businesses choose this
Woodlands is Singapore's gateway to Malaysia and a growing hub for pharmaceuticals and precision manufacturing – AI here supports cross-border trade as much as local operations.
Where Woodlands operators actually lose hours.
The Woodlands Checkpoint handles one of the busiest land border crossings in the world, alongside a growing pharmaceutical and precision engineering cluster. Businesses here want AI that keeps cross-border logistics and manufacturing operations running cleanly.
We work with teams across Woodlands: Woodlands Checkpoint · Woodlands Regional Centre · Admiralty · Sembawang · Marsiling.
How we build semantic search (RAG) for a Woodlands team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Woodlands business, so value lands before the build is finished. Every engagement starts with a short call and a paid discovery if the brief needs one.
AI search over your data.
The outcome for Woodlands teams
The shape of the result for Woodlands teams: Average search time drops from 6 minutes to 12 seconds. Built on Pinecone, hardened with the rest of the stack as it scales.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Woodlands – common questions
What's a typical engagement length for Woodlands businesses?
Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.
Do you do hourly billing or fixed price?
Fixed price for the pilot, every time. After that it's your call – fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.
Do you have proof this works for Woodlands businesses?
Direct case study: Average search time drops from 6 minutes to 12 seconds. Happy to walk you through full numbers on a call.
Can you work with our existing systems?
Yes. The default semantic search (RAG) stack we reach for is Pinecone, Claude, Postgres pgvector, Vercel AI SDK, but we'll bend it around whatever you already run – Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.
Worth a conversation?
Even if you don't end up working with us, you'll leave the call knowing what's worth building.
Get in touch
Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.